In recent years, digital wireless communication systems have been used to convey a variety of information between multiple locations. With digital communications, information is translated into a digital or binary form, referred to as bits, for communications purposes. The transmitter maps this bit stream into a modulated symbol stream, which is detected at the digital receiver and mapped back into bits and information.
In digital wireless communications, the radio environment presents many difficulties that impede successful communications, for example, those caused by the many signal paths traversed by radio signals before arriving at a receiver. One difficulty occurs when the multiple signal paths are much different in length. In this case, time dispersion occurs, in which multiple signal images arrive at the receiver antenna at different times, giving rise to signal echoes. This causes intersymbol interference (ISI), where the echoes of one symbol interfere with subsequent symbols.
Time dispersion can be mitigated by using an equalizer. Common forms of equalization are provided by linear equalizers, decision-feedback equalizers, and maximum-likelihood sequence-estimation (MLSE) equalizers. A linear equalizer tries to undo the effects of the channel by filtering the received signal. A decision-feedback equalizer exploits previous symbol detections to cancel out the intersymbol interference from echoes of these previous symbols. Finally, an MLSE equalizer hypothesizes various transmitted symbol sequences and, with a model of the dispersive channel, determines which hypothesis best fits the received data. These equalization techniques are well known to those skilled in the art, and can be found in standard textbooks such as J.G. Proakis, Digital Communications, 2nd ed., New York: McGraw-Hill, 1989. Equalizers are commonly used in TDMA systems , such as D-AMPS and GSM.
Of the three common equalization techniques, MLSE equalization is preferable from a performance point of view. In the MLSE equalizer, all possible transmitted symbol sequences are considered. For each hypothetical sequence, the received signal samples are predicted using a model of the multipath channel. The difference between the predicted received signal samples and the actual received signal samples, referred to as the prediction error, gives an indication of how good a particular hypothesis is. The squared magnitude of the prediction error is used as a metric to evaluate a particular hypothesis. This metric is accumulated for different hypotheses for use in determining which hypotheses are better. This process is efficiently realized using the Viterbi algorithm, which is a form of dynamic programming.
However, under certain operating conditions, signals arriving at a receiver may not create significant levels of intersymbol interference. When ISI is insignificant, or absent, the equalizer actually adds more noise to the detection statistic than it removes, particularly when the channel varies rapidly. Under these conditions, it would be desirable to switch the equalizer off in favor of another detection device, e.g., a differential detector, which may perform better under non-time dispersive conditions. Moreover, an equalizer is relatively complex computationally compared with a differential detector. Thus, periodically switching off the equalizer in favor of a differential detector would save MIPS which, in turn, would reduce battery consumption.
As another example, in direct sequence CDMA systems, RAKE receivers are commonly employed. However, if too many RAKE taps are employed, performance degrades.
Accordingly, it would be desirable to provide a receiver in which an appropriate detection technique could be dynamically identified and implemented, e.g., a detector which uses an appropriate number of channel taps.